The cgcnn model can accurately learn material properties from graphical representations of atomic crystal structures, called “crystal graphs”. [1]} designed an improved framework of the cgcnn model, called icgcnn, which incorporated Voronoi tessellated crystal structures, 3-body explicit correlations of neighboring atoms, and an optimized chemical representation of interatomic bonds in the crystal graphs, all of which are absent in cgcnn (fig:park2020-icgcnn.png). First, a training/testing dataset consisting of 180000 dft entries from the Open Quantum Materials Database [2]} was created. cgcnn and icgcnn were compared in their accuracy of predicting the thermodynamic stability of inorganic materials. Then, both models were used to conduct separate ml-assisted hitp searches to discover new stable compounds. The new framework was shown to have 20 higher accuracy than those of cgcnn on dft calculated thermodynamic stability and a success rate that is 2.4 times higher than cgcnn. Using icgcnn, they were also able to identify 97 novel stable compounds from 132600 screened ThCR2Si2-type compounds through only 757 dft calculations which corresponds to a success rate that is 130 times higher than that of an undirected hitp search.
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